arXiv:2604. 01730v2 Announce Type: replace Abstract: This paper investigates Koopman operator-based approaches for multivariable control of a two-spool turbofan engine.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2511.22648v2 Announce Type: replace Abstract: A spatially aware dictionary-free eigenfunction discovery (SADFED) framework is proposed for identification of low-rank Koopman models from data wi...
arXiv:2607. 01819v1 Announce Type: cross Abstract: The Koopman operator has gained considerable attention due to its ability to provide a global linear representation of highly complex dynamical systems.
The paper presents a physics‑enhanced machine learning method that combines a long short‑term memory network with an Extended Kalman Filter to identify fast‑varying natural frequencies and damping ratios of Linear Time‑Varying systems. Using vibration data and a physics‑based model, the approach is validated on synthetic data from a 2‑blade offshore wind turbine, achieving a maximum RMS error of 0.0012 Hz for the first Fore‑Aft mode. The study also demonstrates robustness to incorrect damping assumptions and improves damping ratio estimation compared to covariance‑driven stochastic subspace identification.
arXiv:2606. 16171v1 Announce Type: cross Abstract: Multi-fuel compression ignition (CI) engines offer superior power density and fuel flexibility.
The paper introduces a panoramic aerodynamic performance prediction framework for turbomachinery cascades that first predicts basic Navier-Stokes parameters (temperature, pressure, density) and then derives key turbine stage performance metrics. A transformer‑enhanced neural operator (TNO) is trained on Rotor 37 blades to accurately predict transonic compressor blade performance, outperforming state‑of‑the‑art deep learning operators like FNO and DeepONet. The TNO also supports downstream tasks such as sensitivity analysis and optimization, achieving CFD‑like results while cutting computational cost by four orders of magnitude.
arXiv:2603. 29499v2 Announce Type: replace-cross Abstract: Classical proportional--integral--derivative (PID) control remains widely used in industrial control systems, while model predictive control (MPC) is actively studied to achieve higher performance for systems with nonlinear dynamics.